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46 lines
1.6 KiB
Swift
46 lines
1.6 KiB
Swift
//===--- MonteCarloE.swift ------------------------------------------------===//
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//
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// This source file is part of the Swift.org open source project
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//
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// Copyright (c) 2014 - 2021 Apple Inc. and the Swift project authors
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// Licensed under Apache License v2.0 with Runtime Library Exception
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//
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// See https://swift.org/LICENSE.txt for license information
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// See https://swift.org/CONTRIBUTORS.txt for the list of Swift project authors
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//
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//===----------------------------------------------------------------------===//
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// This test measures performance of Monte Carlo estimation of the e constant.
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//
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// We use 'dart' method: we split an interval into N pieces and drop N darts
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// to this interval.
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// After that we count number of empty intervals. The probability of being
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// empty is (1 - 1/N)^N which estimates to e^-1 for large N.
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// Thus, e = N / Nempty.
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import TestsUtils
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public let benchmarks =
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BenchmarkInfo(
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name: "MonteCarloE",
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runFunction: run_MonteCarloE,
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tags: [.validation, .algorithm],
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legacyFactor: 20)
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public func run_MonteCarloE(scale: Int) {
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var lfsr = LFSR()
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let n = 10_000 * scale
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var intervals = [Bool](repeating: false, count: n)
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for _ in 1...n {
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let pos = Int(UInt(truncatingIfNeeded: lfsr.next()) % UInt(n))
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intervals[pos] = true
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}
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let numEmptyIntervals = intervals.filter{!$0}.count
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// If there are no empty intervals, then obviously the random generator is
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// not 'random' enough.
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check(numEmptyIntervals != n)
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let e_estimate = Double(n)/Double(numEmptyIntervals)
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let e = 2.71828
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check(abs(e_estimate - e) < 0.2)
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}
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